DBMS > Apache Phoenix vs. IBM Db2 Event Store vs. MySQL vs. RavenDB vs. Yanza

System Properties Comparison Apache Phoenix vs. IBM Db2 Event Store vs. MySQL vs. RavenDB vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

Distributed Event Store optimized for Internet of Things use cases

Widely used open source RDBMS

Open Source Operational and Transactional Enterprise NoSQL Document Database

Time Series DBMS for IoT Applications

Primary database model

Relational DBMS

Event Store
Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Document store

Time Series DBMS

Secondary database models

Document store
Spatial DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	2.84
Rank	#101	Overall
	#18	Document stores

Website

phoenix.apache.org

www.ibm.com/products/db2-event-store

www.mysql.com

ravendb.net

yanza.com

Technical documentation

phoenix.apache.org

www.ibm.com/docs/en/db2-event-store

dev.mysql.com/doc

ravendb.net/docs

Developer

Apache Software Foundation

IBM

Oracle

since 2010, originally MySQL AB, then Sun

Hibernating Rhinos

Yanza

Initial release

2014

2017

1995

2010

2015

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

2.0

8.4.0, April 2024

5.4, July 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free developer edition available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

AGPL version 3, commercial license available

commercial

free version available

Cloud-based only

Only available as a cloud service

but mainly used as a service provided by Yanza

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Implementation language

Java

C and C++

Server operating systems

Linux
Unix
Windows

Linux

Linux, macOS, Windows for the developer addition

FreeBSD
Linux
OS X
Solaris
Windows

Linux
macOS
Raspberry Pi
Windows

Windows

Data scheme

yes

late-bound, schema-on-read capabilities

yes

schema-free

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

yes

with proprietary extensions

SQL-like query language (RQL)

APIs and other access methods

JDBC

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

HTTP API

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

any language that supports HTTP calls

Server-side scripts

Stored procedures

user defined functions

yes

proprietary syntax

yes

Triggers

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Active-active shard replication

Multi-source replication
Source-replica replication

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Eventual Consistency

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

not for MyISAM storage engine

ACID, Cluster-wide transaction available

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

Authorization levels configured per client per database

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